ML-Images: the largest open-source multi-label image database, including 17,609,752 training and 88,739 validation image URLs, which are annotated with up to 11,166 categories

Resnet-101 model: it is pre-trained on ML-Images, and achieves the top-1 accuracy 80.73% on ImageNet via transfer learning and has reached the highest precision level in the industry.

Now, let us move onto ML-Images and Resnet-101 in detail:

The image data-set ML-Images released by Tencent AI Lab contains 18 million images and more than 11,000 common object categories. The multi-label image data-set in the industry is large enough to meet the needs of general scientific research institutions and small and medium-sized enterprises.

Tencent AI Lab will provide ResNet-101, a deep residual network based on ML-Images training. The model has an excellent visual representation and generalization performance, and has the highest precision in the current model in the industry. It will provide strong support for visual tasks including images, videos, etc., and help image classification, object detection, object tracking, semantics. And will result in Improvement in technical level such as segmentation.

What is the need of ML-Images?

Global tech giants are placing increasing emphasis on their AI architecture, and have built large internal image datasets such as Google’s JFT-300M and Facebook’s Instagram dataset. However, these trained datasets and models are proprietary, outside the reach of general scientific research institutions and SMEs.

ML-Images will be the largest open source multi-label image data set in the industry — capable of meeting the needs of general scientific research institutions and SMEs alike — and may become researchers’ new standard in the field of computer vision.

Currently, the largest available multi-label image data set is Google’s Open Images, which includes 9 million training images and more than 6000 object categories.
Tencent AI Lab's open source ML-Images data-set includes 18 million training images and more than 11,000 common object categories or will become the new industry benchmark data-set.

Tencent AI Lab's open-source "Tencent ML-Images" project demonstrates Tencent's efforts in building the basic capabilities of artificial intelligence and the vision of promoting the common development of the industry through the opening of basic capabilities.

Moving on further:

In addition, the Tencent AI Lab team migrated the ResNet-101 model based on Tencent ML-Images to many other visual tasks, including image object detection, image semantic segmentation, video object segmentation, and video object tracking.

These visual migration tasks further validate the model's powerful visual representation and excellent generalization performance. “Tencent ML-Images” will, therefore, continue to play an important role in more visually relevant products in the future.

Summing up:

Breakthroughs in Deep Learning Networks have shown that technology can be applied across many fields, especially computer vision, where it excels at essential tasks such as classifying, interpreting and generating images and videos. However, to give full play to the visual potential of deep learning requires high-quality training data, an excellent model structure and model training methods, and powerful computing resources and other capabilities.

This step by Tencent is a major step forward in making high-quality training data more accessible.

The ML-Images dataset are published on Tencent’s GitHub page and for more information, one can go through the links mentioned below: